NVIDIA GPU Computing For Chat Conversation Analysis

Chat conversation analysis requires a lot of processing power. And that’s because Deep Learning, the AI technique that underpins chat optimization, involves analyzing large datasets and repeatedly ‘training’ algorithms on these datasets. Computer Engineers have re-purposed computer hardware to facilitate these processor-intensive tasks.

In a traditional computing system, the Central Processing Unit is the system element that executes tasks – most often several tasks simultaneously. If your computer is running slower than desired, you can open up the Central Processing Unit monitor to kill tasks that are occupying your processing power. You may also be familiar with the names of various CPUs, such as Intel Core processors, which are popular for consumer machines.

Although the power and speed of today’s processors is more than sufficient for everyday applications, Artificial Intelligence requires more computing power. To meet this need, the GPU manufacturer, Nvidia, invented a programming model called CUDA (Compute Unified Device Architecture). CUDA harnesses the power of another computing system component, the GPU. The Graphical Processing Unit is usually dedicated to graphics-intensive programs, such as video games, nonlinear video editing, and design applications. But with Nvidia’s CUDA, it’s possible to use GPUs for parallel processing of other applications, such as chat conversation analysis, machine learning, scientific, and engineering computing applications.

Comparison of CPU and GPU size

A CPU consists of four to eight cores, while a GPU consists of hundreds of smaller cores. Together they operate to crunch through the massive data in a chat application. The high compute performance of a GPU is due to its massive architecture.

The point of CUDA is to write code that can run on compatible massively parallel SIMD (Single Instruction Multiple Data) architectures. This includes several GPU types as well as non-GPU hardware such as NVIDIA Tesla. Massively parallel hardware can run a significantly larger number of operations per second than the CPU at a similar financial cost yielding performance improvements of 50× or more when analyzing live chat data. One of the benefits of CUDA over earlier methods of high-performance computing is that a general-purpose language is available. Instead of having to use pixel and vertex shaders to emulate general-purpose computers, the language is based on C with a few additional keywords and concepts. This makes it a fairly easy language for non-GPU programmers to pick up.

CUDA: Standard C Code and Parallel C Code

NVIDIA recently took a giant step toward an accelerator processor architecture customized for artificial intelligence (AI). NVIDIA pioneered the development of artificial neural networks through deep learning with the company’s GPUs and CUDA software platform. NVIDIA accounts for the majority of deep learning networks in use today. However, while the GPU has proven very effective in parallel processing since the addition of cores, TIRIAS Research has maintained that even NVIDIA would need to eventually migrate to architectures dedicated to AI while preserving its tools and ecosystem to advance its platforms further. Nvidia networks are clearly advantageous for processing live chat data and conducting chat conversation analysis that yields actionable insights.

Most recently, NVIDIA developed the next generation GPU architecture called Volta. Although still referred to as a Graphical Processing Unit, Volta is much more. In addition to enhancing the GPU architecture, NVIDIA added 640 new tensor cores capable of processing 4x4x4 matrix multiplies. This provides a specialized mathcore that works in conjunction with the standard GPU CUDA cores to add additional processing for deep learning environments. It also accelerates the process of inferring a value based on a trained model, making it useful as an inference engine. NVIDIA essentially put accelerator cores in an accelerator.

Google took a similar route when in developing its proprietary Tensor Processing Unit (TPU). With the tensor core capabilities incorporated into the NVIDIA SDK libraries, runtimes like cuDNN (a CUDA based library for Deep Neural Networks), and TensorRT (a high performance neural network interface), developers will be able to take advantage of the increase in performance from the tensor cores in their AI frameworks without rewriting their applications. Combining NVIDIA’s Volta and Google’s TPUs will provide huge computation power and can help in deriving insights from large chat data.

About Dr. Michael Housman

Michael has spent his entire career applying state-of-the-art statistical methodologies and econometric techniques to large data-sets in order to drive organizational decision-making and helping companies operate more effectively.
Prior to founding RapportBoost.AI, he was the Chief Analytics Officer at Evolv (acquired by Cornerstone OnDemand for $42M in 2015) where he helped architect a machine learning platform capable of mining databases consisting of hundreds of millions of employee records. He was named a 2014 game changer by Workforce magazine for his work.
Michael is currently an equity advisor for a half-dozen technology companies based out of the San Francisco bay area: hiQ Labs, Bakround, Interviewed, Performiture, Tenacity, Homebase, and States Title. He was on Tony’s advisory board at Boopsie from 2012 onward.
Michael is a noted public speaker and has published his work in a variety of peer-reviewed journals and has had his research profiled by The New York Times, Wall Street Journal, The Economist, and The Atlantic.
Dr. Housman received his A.M. and Ph.D. in Applied Economics and Managerial Science from The Wharton School of the University of Pennsylvania and his A.B. from Harvard University.